Training and test data for retrievals based on MiRAC-P observations during MOSAiC
The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been dev...
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Online Access: | https://dx.doi.org/10.5281/zenodo.5741748 https://zenodo.org/record/5741748 |
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ftdatacite:10.5281/zenodo.5741748 2023-05-15T14:42:09+02:00 Training and test data for retrievals based on MiRAC-P observations during MOSAiC Orlandi, Emiliano Walbröl, Andreas 2021 https://dx.doi.org/10.5281/zenodo.5741748 https://zenodo.org/record/5741748 unknown Zenodo https://dx.doi.org/10.5281/zenodo.5741747 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY MiRAC-P, LHUMPRO, Microwave Radiometer, Remote Sensing, Arctic, MOSAiC, Retrieval, Water Vapor, IWV dataset Dataset 2021 ftdatacite https://doi.org/10.5281/zenodo.5741748 https://doi.org/10.5281/zenodo.5741747 2022-02-08T14:59:33Z The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign. : More information can be found in the following publication to which this dataset belongs to: (work in progress) Dataset Arctic DataCite Metadata Store (German National Library of Science and Technology) Arctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
MiRAC-P, LHUMPRO, Microwave Radiometer, Remote Sensing, Arctic, MOSAiC, Retrieval, Water Vapor, IWV |
spellingShingle |
MiRAC-P, LHUMPRO, Microwave Radiometer, Remote Sensing, Arctic, MOSAiC, Retrieval, Water Vapor, IWV Orlandi, Emiliano Walbröl, Andreas Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
topic_facet |
MiRAC-P, LHUMPRO, Microwave Radiometer, Remote Sensing, Arctic, MOSAiC, Retrieval, Water Vapor, IWV |
description |
The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign. : More information can be found in the following publication to which this dataset belongs to: (work in progress) |
format |
Dataset |
author |
Orlandi, Emiliano Walbröl, Andreas |
author_facet |
Orlandi, Emiliano Walbröl, Andreas |
author_sort |
Orlandi, Emiliano |
title |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_short |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_full |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_fullStr |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_full_unstemmed |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_sort |
training and test data for retrievals based on mirac-p observations during mosaic |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://dx.doi.org/10.5281/zenodo.5741748 https://zenodo.org/record/5741748 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://dx.doi.org/10.5281/zenodo.5741747 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.5741748 https://doi.org/10.5281/zenodo.5741747 |
_version_ |
1766313851345698816 |